Sustained and Enhanced Nucleate Boiling Using Hierarchical Architectures at Large Superheats
Bibliographic record
Abstract
Droplet boiling is a common occurrence in many industrial processes, but it can be hindered by the Leidenfrost effect. The Leidenfrost point (LP), defined as the temperature at which an accumulated and stagnant vapor forms between the liquid and the heated solid, consequently deteriorates cooling performance. In this study, inspired by nature, we demonstrate how using a nano-micro hierarchical triple-passage architecture with a higher aspect ratio enhances both vapor and liquid spreading dynamics, boosts heat transfer, and thus elevates the LP. Our results show that the LP is promoted to 273°C, which is a delay of approximately 130°C compared to the LP of 145°C on a copper surface. Through theoretical analysis, we develop a multi-force competition model to reveal the underlying physics of this sustained nucleate boiling. Our findings challenge traditional wisdom, indicating that lower impact velocities of a droplet, though sacrificing the convection, delay the LP through impact pattern manipulation. Additionally, we adopt a physics-informed deep neural network framework to accurately model the nonlinear behavior of droplet boiling (from nucleate boiling to LP) on various surfaces within an ≈11% error. The results here have potential applications in designing more efficient droplet-based boiling heat transfer devices and in controlling droplet boiling at high temperatures.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".